2014
DOI: 10.1089/aid.2014.0173
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Impact of Sampling Density on the Extent of HIV Clustering

Abstract: Identifying and monitoring HIV clusters could be useful in tracking the leading edge of HIV transmission in epidemics. Currently, greater specificity in the definition of HIV clusters is needed to reduce confusion in the interpretation of HIV clustering results. We address sampling density as one of the key aspects of HIV cluster analysis. The proportion of viral sequences in clusters was estimated at sampling densities from 1.0% to 70%. A set of 1,248 HIV-1C env gp120 V1C5 sequences from a single community in… Show more

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Cited by 63 publications
(74 citation statements)
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“…A larger set of available HIV-1C sequences (n ϭ 547) included matched viral sequences for amplicon 1 and the V1C5 region of gp120 generated in our previous studies (34,35,80). Clustering patterns were compared for two long loci, amplicon 1 and concatenated amplicon 1 plus V1C5, and for two short regions across the HIV-1 genome, ViroSeq and V1C5.…”
Section: Resultsmentioning
confidence: 99%
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“…A larger set of available HIV-1C sequences (n ϭ 547) included matched viral sequences for amplicon 1 and the V1C5 region of gp120 generated in our previous studies (34,35,80). Clustering patterns were compared for two long loci, amplicon 1 and concatenated amplicon 1 plus V1C5, and for two short regions across the HIV-1 genome, ViroSeq and V1C5.…”
Section: Resultsmentioning
confidence: 99%
“…A combination of conserved (amplicon 1) and variable (amplicon 2) regions could help to deal with different and/or unknown stages of HIV infection in an analyzed set of viral sequences. The choice of a particular bootstrap value and filtering by the threshold of pairwise distances and/or internode certainty (94,95) could depend on the specific scientific question and take into account the specifics of an analyzed set of sequences, including sampling density (35).…”
Section: Discussionmentioning
confidence: 99%
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“…It is possible that patterns of HIV clustering might differ between modes of viral transmission and HIV-1 subtypes associated with particular modes of transmission. As we demonstrated recently, 73 sampling density is another critical factor affecting the extent of HIV clustering.…”
Section: Discussionmentioning
confidence: 64%
“…This kind of growth curve has three qualitative phases: exponential growth, linear growth, and a slow final phase when the susceptible population is almost depleted. The waiting times until the next transmission, which determine the coalescence times in the tree, are MSM/USA (Little et al 2014) MSM/Taiwan (Kao et al 2011) MSM/Beijing (Wang et al 2015) HET/Uganda (Grabowski et al 2014) HET/Malawi (McCormack et al 2002) HET/Botswana (Novitsky et al 2013(Novitsky et al & 2014 IDU/Romania (Niculescu et al 2015) IDU/Estonia (Zetterberg et al 2004) dependent on the growth phase of the epidemic. Therefore, we hypothesize that it is the growth phase at the time of sampling which most affects tree shape, rather than the specific values of I or N ( Supplementary Fig.…”
Section: Analysis Of Ba Model With Synthetic Datamentioning
confidence: 99%